[dvcat]

This makes a lot of sense. I was working through a couple of the assignments last night when I realized that a lot of the questions seemed to test reading comprehension rather than proper understanding of the material. I had a while back used Prof. Ng's video lectures and notes (available on his site) as a supplement to my own machine learning class and those are designed to both stretch your mind and test your understanding. I was kind of hoping that that level of quality was being shared with the rest of the world but that doesn't seem to be the case :(

[aheilbut]

That's exactly the discussion I had that led me to go and want to check out the site. Apparently the pace and nature of the (AI class) homeworks are quite different in the actual Stanford course.

[robryan]

Guess it has a lot to do with having to be corrected automatically rather than a professor going through your process on questions with individual correction.

That might actually be an interesting AI research area, making computers better at marking based on process the student used rather than just the final answer. Wonder if much has been done in that area as it will become more important as more learning goes online and people want more than multiple choice/ final numerical answer type stuff.

[dhotson]

They can probably infer it based on the distribution of answers.

They can probably infer the mistakes people made by looking at the really common but incorrect answers. I'm guessing that lots of people get the same wrong answers as each other.

[webspiderus]

for what it's worth, I'm taking Stanford's version of the ML class that Prof. Ng is offering, and aside from being a couple of weeks and ahead and having to do a final project, it is pretty much the same offering as that available the outside world.

[amirmc]

I read that some things were dropped from the online version. Specifically, reinforcement learning. Is that still part of your syllabus?

Edit: Here's the link. Not sure if you can see it without being logged in. http://www.ml-class.org/course/qna/view?id=58